package org.codelibs.fess.suggest.request;

/**
 * The Response interface serves as a marker interface for all response types
 * in the suggestion system. Implementations of this interface are used to
 * encapsulate the results of various operations and queries within the system.
 *
 * <p>Classes implementing this interface should provide specific details and
 * data relevant to the type of response they represent.</p>
 */
public interface Response {

    val request = Request(server.url("/"))

    val call = client.newCall(request)
    val response1 = call.execute()

    val cloned = call.clone()
    val response2 = cloned.execute()

    assertThat("abc").isEqualTo(response1.body.string())
    assertThat("def").isEqualTo(response2.body.string())
  }

  @Test
  @Flaky
  fun testMockWebserverRequest() {
    enableTls()

* Resolves an issue serving aggregated APIs backed by services that respond to requests to `/` with non-2xx HTTP responses ([#79895](https://github.com/kubernetes/kubernetes/pull/79895), [@deads2k](https://github.com/deads2k))

E as ferramentas também ficarão felizes porque `RedirectResponse` e ​​`JSONResponse` são subclasses de `Response`, então a anotação de tipo está correta.

### Anote uma subclasse de Response { #annotate-a-response-subclass }

Você também pode usar uma subclasse de `Response` na anotação de tipo:

因此，**FastAPI** 将会负责过滤掉未在输出模型中声明的所有数据（使用 Pydantic）。

## 在文档中查看

当你查看自动化文档时，你可以检查输入模型和输出模型是否都具有自己的 JSON Schema：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

并且两种模型都将在交互式 API 文档中使用：

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## 响应模型编码参数

你的响应模型可以具有默认值，例如：

{* ../../docs_src/response_model/tutorial004.py hl[11,13:14] *}

# Código de status de resposta { #response-status-code }

Da mesma forma que você pode especificar um modelo de resposta, você também pode declarar o código de status HTTP usado para a resposta com o parâmetro `status_code` em qualquer uma das *operações de rota*:

* `@app.get()`
* `@app.post()`
* `@app.put()`
* `@app.delete()`
* etc.

{* ../../docs_src/response_status_code/tutorial001_py39.py hl[6] *}

/// note | Nota

## ドキュメントを見る

自動ドキュメントを見ると、入力モデルと出力モデルがそれぞれ独自のJSON Schemaを持っていることが確認できます。

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

そして、両方のモデルは、対話型のAPIドキュメントに使用されます:

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## レスポンスモデルのエンコーディングパラメータ

レスポンスモデルにはデフォルト値を設定することができます:

{* ../../docs_src/response_model/tutorial004.py hl[11,13,14] *}

            response.readBytesWireFormat(buffer1, 0);
            assertEquals(1024, response.getCount());
            assertEquals(512, response.getRemaining());

            // Second read - should update values
            byte[] buffer2 = createValidWriteResponse(2048, 1024);
            response.readBytesWireFormat(buffer2, 0);
            assertEquals(2048, response.getCount());

    public void test_constructorWithoutErrors() throws Exception {
        SuggestDeleteResponse response = new SuggestDeleteResponse(null, 100);

        assertNotNull(response);
        assertEquals(100, response.getTook());
        assertFalse(response.hasError());
        assertNotNull(response.getErrors());
        assertEquals(0, response.getErrors().size());
    }

    @Test

따라서 **FastAPI**는 출력 모델에서 선언하지 않은 모든 데이터를 (Pydantic을 사용하여) 필터링합니다.

## 문서에서 보기

자동 생성 문서를 보면 입력 모델과 출력 모델이 각자의 JSON 스키마를 가지고 있음을 확인할 수 있습니다:

<img src="/img/tutorial/response-model/image01.png">

그리고 두 모델 모두 대화형 API 문서에 사용됩니다:

<img src="/img/tutorial/response-model/image02.png">

## 응답 모델 인코딩 매개변수

응답 모델은 아래와 같이 기본값을 가질 수 있습니다:

{* ../../docs_src/response_model/tutorial004.py hl[11,13:14] *}